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Fast Moment Estimation for Generalized Latent Dirichlet Models

Author(s): Zhao, Shiwen; Engelhardt, Barbara E; Mukherjee, Sayan; Dunson, David B

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dc.contributor.authorZhao, Shiwen-
dc.contributor.authorEngelhardt, Barbara E-
dc.contributor.authorMukherjee, Sayan-
dc.contributor.authorDunson, David B-
dc.date.accessioned2021-10-08T19:49:07Z-
dc.date.available2021-10-08T19:49:07Z-
dc.date.issued2018en_US
dc.identifier.citationZhao, Shiwen, Barbara E. Engelhardt, Sayan Mukherjee, and David B. Dunson. "Fast Moment Estimation for Generalized Latent Dirichlet Models." Journal of the American Statistical Association 113, no. 524 (2018): pp. 1528-1540. doi:10.1080/01621459.2017.1341839en_US
dc.identifier.issn0162-1459-
dc.identifier.urihttps://arxiv.org/pdf/1603.05324.pdf-
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/pr1rr9c-
dc.description.abstractWe develop a generalized method of moments (GMM) approach for fast parameter estimation in a new class of Dirichlet latent variable models with mixed data types. Parameter estimation via GMM has computational and statistical advantages over alternative methods, such as expectation maximization, variational inference, and Markov chain Monte Carlo. A key computational advantage of our method, Moment Estimation for latent Dirichlet models (MELD), is that parameter estimation does not require instantiation of the latent variables. Moreover, performance is agnostic to distributional assumptions of the observations. We derive population moment conditions after marginalizing out the sample-specific Dirichlet latent variables. The moment conditions only depend on component mean parameters. We illustrate the utility of our approach on simulated data, comparing results from MELD to alternative methods, and we show the promise of our approach through the application to several datasets. Supplementary materials for this article are available online.en_US
dc.format.extent1528 - 1540en_US
dc.language.isoen_USen_US
dc.relation.ispartofJournal of the American Statistical Associationen_US
dc.rightsAuthor's manuscripten_US
dc.titleFast Moment Estimation for Generalized Latent Dirichlet Modelsen_US
dc.typeJournal Articleen_US
dc.identifier.doi10.1080/01621459.2017.1341839-
dc.identifier.eissn1537-274X-
pu.type.symplectichttp://www.symplectic.co.uk/publications/atom-terms/1.0/journal-articleen_US

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